Radiotherapy treatment plans using differentiable dose functions

ABSTRACT

Techniques for generating a radiotherapy treatment plan parameter are provided. The techniques include receiving radiotherapy treatment plan information; processing the radiotherapy treatment plan information to estimate one or more radiotherapy treatment plan parameters based on a process that depends on the output of a subprocess that estimates a derivative of a dose calculation; and generating a radiotherapy treatment plan using the estimated one or more radiotherapy treatment plan parameters.

CLAIM FOR PRIORITY

This application is a continuation of U.S. application Ser. No.16/512,938, filed Jul. 16, 2019, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to generating a radiation therapy orradiotherapy treatment plan.

BACKGROUND

Radiotherapy is used to treat cancers and other ailments in mammalian(e.g., human and animal) tissue. The direction and shape of theradiation beam should be accurately controlled to ensure the tumorreceives the prescribed radiation, and the placement of the beam shouldbe such as to minimize damage to the surrounding healthy tissue (oftencalled the organ(s) at risk (OARs)). Treatment planning can be used tocontrol radiation beam parameters, and a radiotherapy device effectuatesa treatment by delivering a spatially varying dose distribution to thepatient.

Traditionally, for each patient, a radiation therapy treatment plan(“treatment plan”) may be created using an optimization technique basedon clinical and dosimetric objectives and constraints (e.g., themaximum, minimum, and mean doses to the tumor and critical organs). Thetreatment planning procedure may include using a three-dimensional (3D)image of the patient to identify a target region (e.g., the tumor) andto identify critical organs near the tumor. Creation of a treatment plancan be a time-consuming process where a planner tries to comply withvarious treatment objectives or constraints (e.g., dose volume histogram(DVH) objectives), taking into account their individual importance(e.g., weighting) in order to produce a treatment plan which isclinically acceptable. This task can be a time-consuming,trial-and-error process that is complicated by the various OARs, becauseas the number of OARs increases (e.g., 21 are commonly segmented in ahead-and-neck treatment), so does the complexity of the process. OARsdistant from a tumor may be easily spared from radiation, while OARsclose to or overlapping a target tumor may be difficult to spare.

Segmentation may be performed to identify the OARs and the area to betreated (for example, a planning target volume (PTV)). Aftersegmentation, a dose plan may be created for the patient indicating thedesirable amount of radiation to be received by the, one or more, PTV(e.g., target) and/or the OARs. A PTV may have an irregular volume andmay be unique as to its size, shape, and position. A treatment plan canbe calculated after optimizing a large number of plan parameters toensure that enough dose is provided to the PTV(s) while as low a dose aspossible is provided to surrounding healthy tissue. Therefore, aradiation therapy treatment plan may be determined by balancingefficient control of the dose to treat the tumor against sparing anyOAR. Typically, the quality of a radiation treatment plan may dependupon the level of experience of the planner. Further complications maybe caused by anatomical variations between patients.

Overview

In some embodiments, a computer-implemented method, non-transitorycomputer readable medium, and a system comprising a memory and processorare provided for generating a radiotherapy treatment plan parameter by:receiving, by processor circuitry, radiotherapy treatment planinformation; processing, by the processor circuitry, the radiotherapytreatment plan information to estimate one or more radiotherapytreatment plan parameters based on a process that depends on the outputof a subprocess that estimates a derivative of a dose calculation,wherein the derivative of the dose calculation is used in anoptimization process or a machine learning model that is based on a lossfunction, wherein the derivative of the dose calculation is computedwith respect to at least one of one or more radiation parameters or oneor more geometry parameters of a radiotherapy treatment device; andgenerating, by the processor circuitry, a radiotherapy treatment planusing the estimated one or more radiotherapy treatment plan parameters.

In some embodiments, the radiotherapy treatment plan informationincludes at least one of a magnetic resonance (MR) image, a cone-beamcomputed tomography (CBCT) image, a computed tomography (CT) image, adose distribution, a segmentation map and a distance map.

In some embodiments, the estimated one or more radiotherapy treatmentplan parameters comprises at least one of a synthetic computedtomography (sCT) image and a dose distribution.

In some embodiments, processing the radiotherapy treatment planinformation comprises processing the radiotherapy treatment planinformation with a machine learning model to generate the one or moreestimated radiotherapy treatment plan parameters, wherein the machinelearning model is trained to establish a relationship between aplurality of training radiotherapy treatment plan information and aplurality of training radiotherapy treatment plan parameters, based ondose calculations using the plurality of training radiotherapy treatmentplan information, wherein the machine learning model includes a deepneural network, wherein the plurality of training radiotherapy treatmentplan information comprises at least one of a training MR image, atraining CBCT image, a training CT image, a first training dosedistribution, a training segmentation map or a training distance map,and wherein the plurality of training radiotherapy treatment planparameters comprises at least one of a training synthetic computedtomography (sCT) image or a second training dose distribution.

In some embodiments, the machine learning model is trained by: obtaininga first batch of training data pairs comprising a given set of trainingradiotherapy treatment plan information and a set of correspondingtraining radiotherapy treatment plan parameters; processing the givenset of training radiotherapy treatment plan information with the machinelearning model to generate an intermediate radiotherapy treatment planparameter; computing a derivative of the loss function based on theintermediate radiotherapy treatment plan parameter; and updatingparameters of the machine learning model based on the computedderivative of the loss function.

In some embodiments, the computer-implemented method, non-transitorycomputer readable medium, and the system perform operations comprising:computing a first dose based on the set of corresponding trainingradiotherapy treatment plan parameters; computing a second dose based onthe intermediate radiotherapy treatment plan parameter; and applying thefirst and second doses to the loss function before computing thederivative of the loss function.

In some embodiments, the dose calculation includes at least one or acombination of a Monte Carlo simulation or a deterministic calculationusing a point kernel convolution algorithm, a pencil kernel algorithm,or a Boltzmann equation solver.

In some embodiments, the optimization process comprises a radiotherapytreatment plan optimization problem that comprises decision variablesspecifying at least one of an isocenter location or a beam angle for theradiotherapy treatment device.

In some embodiments, wherein the derivative of the dose calculation is afirst-order derivative. In some implementations, the first-orderderivative is not a constant value.

In some embodiments, the subprocess that estimates a derivativecomprises an automatic differentiation process.

The above overview is intended to provide an overview of subject matterof the present patent application. It is not intended to provide anexclusive or exhaustive explanation of the disclosure. The detaileddescription is included to provide further information about the presentpatent application.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsdescribe substantially similar components throughout the several views.Like numerals having different letter suffixes represent differentinstances of substantially similar components. The drawings illustrategenerally, by way of example but not by way of limitation, variousembodiments discussed in the present document.

FIG. 1 illustrates an exemplary radiotherapy system adapted forperforming treatment plan generation processing, according to someexamples of the disclosure.

FIG. 2A illustrates an exemplary image-guided radiotherapy device,according to some examples of the disclosure.

FIG. 2B illustrates a radiation therapy device, a Gamma Knife, accordingto some examples of the disclosure.

FIG. 3 illustrates an exemplary data flow for training and use of amachine learning technique based on a derivative of a dose, according tosome examples of the disclosure.

FIGS. 4-9 illustrate flowcharts of exemplary operations for estimatingradiotherapy treatment plan parameters based on a derivative of a dosecalculation, according to some examples of the disclosure.

DETAILED DESCRIPTION

The present disclosure includes various techniques to generateradiotherapy treatment plans by using a machine learning (ML) model orsolving an optimization problem based on a derivative of a dosecalculation. As one example, the ML model can be trained to estimate oneor more parameters of the radiotherapy treatment plan (e.g., a syntheticCT image) from radiotherapy treatment plan information (e.g., a CT or MRimage) based on a loss function that takes into account a derivative ofa dose. As another example, one or more parameters (e.g., a constraintand or a decision variable) of an optimization problem can consider aderivative of a dose to generate a solution to the optimization problemto provide one or more parameters of the radiotherapy treatment plan(e.g., radiotherapy device parameters or control points). The technicalbenefits include reduced computing processing times to generateradiotherapy treatment plans and solving radiotherapy treatment planoptimization problems and accompanying improvements in processing,memory, and network resources used to generate radiotherapy treatmentplans and solve radiotherapy treatment plan optimization problems. Theseradiotherapy treatment plans may be applicable to a variety of medicaltreatment and diagnostic settings or radiotherapy treatment equipmentand devices. Accordingly, in addition to these technical benefits, thepresent techniques may also result in many apparent medical treatmentbenefits (including improved accuracy of radiotherapy treatment, reducedexposure to unintended radiation, and the like).

Radiotherapy is one of the primary methods for treating cancer and isrecommended for over 50% of all cancer patients. Treatment plans arecreated through a complex design process involving a mathematicaloptimization problem that captures the desirable characteristics of thedose delivery—typically requiring a sufficiently high dose to the targetwhile minimizing the dose to healthy tissue. The overall structure ofthe optimization problem is the same for most forms of radiotherapy,including linac-based treatments (3D-CRT, IMRT, VMAT), protontreatments, Gamma Knife radiosurgery, and brachytherapy. The end resultis the radiotherapy device configuration (e.g., control points) requiredto deliver the dose distribution.

Simulation of the absorbed radiation dose in a volume (e.g., dosecalculations) is typically an important factor in generatingradiotherapy treatment plans. The dose is used as a reliable andverifiable link between the chosen treatment parameters and the observedclinical outcome for a specified treatment technique. The result of acareful treatment plan optimization is a set of treatment variables,such as prescribed dose level for the tumor, the number of therapeuticbeams, their angles of incidence, and a set of intensity amplitudes.Several dose calculation processes exist including stochastic processes,such as Monte Carlo simulation, and deterministic processes such aspoint kernel convolution algorithms, pencil kernel algorithms, orBoltzmann equation solvers.

Deterministic analytical dose calculations can be divided into twotypes: point kernel convolution algorithms and pencil kernel algorithms.Point kernel methods first calculate the total energy released per mass(TERMA) in the patient with a raytrace method and a subsequentconvolution (or superposition) with the point kernel to model the dosedistribution from the generated electrons and scattered photons. Theconvolution with the point kernel redistributes the TERMA into thecorrect dose distribution and is the most time-consuming step. A commonimplementation of the convolution step is the collapsed cone algorithm.

The Boltzmann transport equation (BTE) is the governing equation whichdescribes the macroscopic behavior of radiation particles (neutrons,photons, electrons, etc.) as they travel through and interact withmatter. The LBTE is the linearized form of the BTE, which assumes thatradiation particles only interact with the matter they are passingthrough, and not with each other, and is valid for conditions withoutexternal magnetic fields. For a given volumetric domain of matter,subject to a radiation source, under the above conditions the solutionto the LBTE would give an “exact” description of the dose within thedomain. However, since closed form solutions (analytic solutions) to theLBTE can only be obtained for a few simplified problems, the LBTE istypically solved in an open form, or non-analytic, manner. There are twogeneral approaches to obtaining open form solutions to the LBTE. Thefirst approach is the widely known Monte Carlo method. Monte Carlomethods do not explicitly solve the LBTE; they indirectly obtain thesolution to this equation. The second approach is to explicitly solvethe LBTE using numerical methods.

Current planning software typically solve the minimization problem usingstandard mathematical optimization methods. These can be slow, causingunnecessary waiting for patients and clinicians. Future applicationsutilizing real-time imaging could even require real-time treatmentplanning, which cannot be performed using conventional optimizationproblem solvers. Typical optimization problem solvers take into accountdose after generating a solution which results in certain inefficienciesif the dose fails to meet certain constraints. Namely, optimizationproblem solvers fail to consider a dose calculation in generatingsolutions resulting in inaccurate and ineffective treatment planparameters. Also, certain ML models are used to generate some or allparameters of the radiotherapy treatment plan but such ML models alsofail to consider dose calculations. As a result, the parameters providedby the ML models may end up being inaccurate and fail to satisfy certaindose constraints resulting in inefficiencies.

The disclosed techniques address these challenges and increase the speedand efficiency at which radiotherapy treatment plan is generated bytaking into account a derivative of a dose calculation in training a MLmodel or optimizing an optimization problem. In some implementations,the disclosed techniques consider dose calculations given a set oftreatment variables to verify or update radiotherapy treatment planparameters and settings. For example, an ML model can be trained basedon a dose based loss function to generate a synthetic CT image from anMRI or cone-beam computed tomography (CBCT) image to use in treatmentplan optimization. As another example, one or more parameters of aradiotherapy treatment plan optimization problem can be based on aderivative of a dose calculation to provide beam angle optimization (forLinacs) or isocenter selection (for Gamma Knife). As another example,control points of a radiotherapy device can be estimated by applying anML model that is trained based on a dose based loss function to amedical image (e.g., MRI, CT, CBCT, and/or sCT image). As anotherexample, a computationally costly dose calculation can be processed moreefficiently and faster using an ML model that is trained to replicatethe dose calculation of the computationally costly function. Byincreasing the speed and accuracy at which radiotherapy treatment planparameters are generated, the disclosed techniques may enable real-timetreatment planning to be performed and reduce wait time for patients andclinicians.

Specifically, the disclosed techniques receive radiotherapy treatmentplan information and process the radiotherapy treatment plan informationto estimate one or more radiotherapy treatment plan parameters based ona process that depends on the output of a subprocess that estimates aderivative of a dose calculation. A radiotherapy treatment plan isgenerated using the estimated one or more radiotherapy treatment planparameters. In general, the disclosed techniques can be applied to aradiotherapy treatment plan application that utilizes a derivative ofdose with respect to a treatment variable.

As used herein, the term “derivative” refers to any of derivative,subderivative, gradient, subgradient, directional derivative, Jacobian,Fréchet derivative, higher-order derivative, differential operator,Radon-Nikodym derivative, Schwarzian derivative, Wirtinger derivative,H-derivative, covariant derivative, variational derivative, functionalderivative, and/or any combination thereof.

FIG. 1 illustrates an exemplary radiotherapy system 100 adapted toperform radiotherapy plan processing operations using one or more of theapproaches discussed herein. These radiotherapy plan processingoperations are performed to enable the radiotherapy system 100 toprovide radiation therapy to a patient based on specific aspects ofcaptured medical imaging data and therapy dose calculations orradiotherapy machine configuration parameters. Specifically, thefollowing processing operations may be implemented as part of thetreatment processing logic 120. It will be understood, however, thatmany variations and use cases of the following trained models andtreatment processing logic 120 may be provided, including in dataverification, visualization, and other medical evaluative and diagnosticsettings.

The radiotherapy system 100 includes a radiotherapy processing computingsystem 110 which hosts treatment processing logic 120. The radiotherapyprocessing computing system 110 may be connected to a network (notshown), and such network may be connected to the Internet. For instance,a network can connect the radiotherapy processing computing system 110with one or more private and/or public medical information sources(e.g., a radiology information system (RIS), a medical record system(e.g., an electronic medical record (EMR)/electronic health record (EHR)system), an oncology information system (OIS)), one or more image datasources 150, an image acquisition device 170 (e.g., an imagingmodality), a treatment device 180 (e.g., a radiation therapy device),and a treatment data source 160.

As an example, the radiotherapy processing computing system 110 can beconfigured to receive a treatment goal of a subject (e.g., from one ormore MR images) and generate a radiotherapy treatment plan by executinginstructions or data from the treatment processing logic 120, as part ofoperations to generate treatment plans to be used by the treatmentdevice 180 and/or for output on device 146. In an embodiment, thetreatment processing logic 120 solves an optimization problem and/orapplies an ML model to the treatment goal to generate the radiotherapytreatment plan. In an example, the treatment processing logic 120receives radiotherapy treatment plan information and processes theradiotherapy treatment plan information to estimate one or moreradiotherapy treatment plan parameters based on a process that dependson the output of a subprocess that estimates a derivative of a dosecalculation. The subprocess that estimates the derivative of the dosecalculation can be performed based on any one or combination oftechniques including symbolic differentiation; numerical differentiation(e.g., finite differences); and automatic differentiation by applyingthe chain rule on the elementary arithmetic operations that underpinevery computer program, no matter how complicated. Then the treatmentprocessing logic 120 generates a radiotherapy treatment plan using theestimated one or more radiotherapy treatment plan parameters.

A generic radiotherapy treatment plan optimization problem can bedefined as Equation 1:

$\begin{matrix}{{\underset{x \in X}{minimize}\;{f(x)}}{{{subject}\mspace{14mu}{to}\mspace{14mu} x} \in \Omega}} & (1)\end{matrix}$

where ƒ: X→

is the objective function, x∈X is the decision variables and Ω⊆X is theset of feasible variables. In general, the function ƒ can be nonlinearand the set Ω non-convex. The optimization problems are typically solvedusing some form of iterative scheme. For example, in case ƒ is smoothand convex, and Ω is convex, then the projected gradient scheme could beused to solve eq. (1) and reads as follows:

x _(n+1)=proj_(Ω)(x _(n)−η∇ƒ(x _(n)))

where proj_(Ω): X→X is the projection onto Ω, η∈

is a stepsize and ∇ƒ: X→X the gradient. While these algorithms aretypically provably convergent (e.g., given enough time (and correctparameter choices), the algorithm will converge to a minimizer).According to the disclosed techniques, one or more parameters of theoptimization problem of Equation 1 can be computed or provided by aderivative of a dose calculation. In doing so, updates to the decisionvariables of the radiotherapy treatment plan optimization problem aremore accurate with respect to the dose to a patient which makes findinga solution faster.

Particularly, the disclosed embodiments enhance the speed and efficiencyof solving the optimization problem and increase the accuracy of thesolution by utilizing a dose calculation as one or more of theparameters of the optimization problem. In general, a dose D depends ontwo broad categories of treatment variables, any one of which can beconsidered by the parameters of Equation 1. Such treatment variablesinclude radiation parameters Ψ and geometry parameters Ω. The radiationparameters Ψ can include the number of therapeutic beams, angles ofincidence, intensity amplitudes, isocentre locations, collimatorconfigurations, dwell times, and so forth. The geometry parameters SIcan include patient position, shape, density, material decomposition,and so forth. In some implementations, the dose is represented byEquation 2 which can be included as one or more of the parameters of theoptimization problem of Equation 1:

$\begin{matrix}{D = {D\left( {\Psi,\ \Omega} \right)}} & (2)\end{matrix}$

The radiotherapy processing computing system 110 may include processingcircuitry 112, memory 114, a storage device 116, and other hardware andsoftware-operable features such as a user interface 142, a communicationinterface (not shown), and the like. The storage device 116 may storetransitory or non-transitory computer-executable instructions, such asan operating system, radiation therapy treatment plans, training data,software programs (e.g., image processing software, image or anatomicalvisualization software, artificial intelligence (AI) or MLimplementations and algorithms such as provided by deep learning models,ML models, and neural networks (NNs), etc.), and any othercomputer-executable instructions to be executed by the processingcircuitry 112.

In an example, the processing circuitry 112 may include a processingdevice, such as one or more general-purpose processing devices such as amicroprocessor, a central processing unit (CPU), a graphics processingunit (GPU), an accelerated processing unit (APU), or the like. Moreparticularly, the processing circuitry 112 may be a complex instructionset computing (CISC) microprocessor, a reduced instruction set computing(RISC) microprocessor, a very long instruction Word (VLIW)microprocessor, a processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Theprocessing circuitry 112 may also be implemented by one or morespecial-purpose processing devices such as an application-specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), a System on a Chip (SoC), or the like.

As would be appreciated by those skilled in the art, in some examples,the processing circuitry 112 may be a special-purpose processor ratherthan a general-purpose processor. The processing circuitry 112 mayinclude one or more known processing devices, such as a microprocessorfrom the Pentium™, Core™ Xeon™, or Itanium® family manufactured byIntel™, the Turion™, Athlon™ Sempron™, Opteron™, FX™, Phenom™ familymanufactured by AMD™, or any of various processors manufactured by SunMicrosystems. The processing circuitry 112 may also include graphicalprocessing units such as a GPU from the GeForce®, Quadro®, Tesla® familymanufactured by Nvidia™, GMA, Iris™ family manufactured by Intel™, orthe Radeon™ family manufactured by AMD™. The processing circuitry 112may also include accelerated processing units such as the Xeon Phi™family manufactured by Intel™. The disclosed embodiments are not limitedto any type of processor(s) otherwise configured to meet the computingdemands of identifying, analyzing, maintaining, generating, and/orproviding large amounts of data or manipulating such data to perform themethods disclosed herein. In addition, the term “processor” may includemore than one physical (circuitry-based) or software-based processor(for example, a multi-core design or a plurality of processors eachhaving a multi-core design). The processing circuitry 112 can executesequences of transitory or non-transitory computer program instructions,stored in memory 114, and accessed from the storage device 116, toperform various operations, processes, and methods that will beexplained in greater detail below. It should be understood that anycomponent in system 100 may be implemented separately and operate as anindependent device and may be coupled to any other component in system100 to perform the techniques described in this disclosure.

The memory 114 may comprise read-only memory (ROM), a phase-changerandom access memory (PRAM), a static random access memory (SRAM), aflash memory, a random access memory (RAM), a dynamic random accessmemory (DRAM) such as synchronous DRAM (SDRAM), an electrically erasableprogrammable read-only memory (EEPROM), a static memory (e.g., flashmemory, flash disk, static random access memory) as well as other typesof random access memories, a cache, a register, a compact disc read-onlymemory (CD-ROM), a digital versatile disc (DVD) or other opticalstorage, a cassette tape, other magnetic storage device, or any othernon-transitory medium that may be used to store information includingimages, training data, one or more ML model(s) or technique(s)parameters, data, or transitory or non-transitory computer executableinstructions (e.g., stored in any format) capable of being accessed bythe processing circuitry 112, or any other type of computer device. Forinstance, the computer program instructions can be accessed by theprocessing circuitry 112, read from the ROM, or any other suitablememory location, and loaded into the RAM for execution by the processingcircuitry 112.

The storage device 116 may constitute a drive unit that includes atransitory or non-transitory machine-readable medium on which is storedone or more sets of transitory or non-transitory instructions and datastructures (e.g., software) embodying or utilized by any one or more ofthe methodologies or functions described herein (including, in variousexamples, the treatment processing logic 120 and the user interface142). The instructions may also reside, completely or at leastpartially, within the memory 114 and/or within the processing circuitry112 during execution thereof by the radiotherapy processing computingsystem 110, with the memory 114 and the processing circuitry 112 alsoconstituting transitory or non-transitory machine-readable media.

The memory 114 and the storage device 116 may constitute anon-transitory computer-readable medium. For example, the memory 114 andthe storage device 116 may store or load transitory or non-transitoryinstructions for one or more software applications on thecomputer-readable medium. Software applications stored or loaded withthe memory 114 and the storage device 116 may include, for example, anoperating system for common computer systems as well as forsoftware-controlled devices. The radiotherapy processing computingsystem 110 may also operate a variety of software programs comprisingsoftware code for implementing the treatment processing logic 120 andthe user interface 142. Further, the memory 114 and the storage device116 may store or load an entire software application, part of a softwareapplication, or code or data that is associated with a softwareapplication, which is executable by the processing circuitry 112. In afurther example, the memory 114 and the storage device 116 may store,load, and manipulate one or more radiation therapy treatment plans,imaging data, segmentation data, treatment visualizations, histograms ormeasurements, one or more AI model data (e.g., weights and parameters ofthe ML model(s) of the disclosed embodiments), training data, labels andmapping data, and the like. It is contemplated that software programsmay be stored not only on the storage device 116 and the memory 114 butalso on a removable computer medium, such as a hard drive, a computerdisk, a CD-ROM, a DVD, a Blu-Ray DVD, USB flash drive, a SD card, amemory stick, or any other suitable medium; such software programs mayalso be communicated or received over a network.

Although not depicted, the radiotherapy processing computing system 110may include a communication interface, network interface card, andcommunications circuitry. An example communication interface mayinclude, for example, a network adaptor, a cable connector, a serialconnector, a USB connector, a parallel connector, a high-speed datatransmission adaptor (e.g., such as fiber, USB 3.0, thunderbolt, and thelike), a wireless network adaptor (e.g., such as a IEEE 802.11/Wi-Fiadapter), a telecommunication adapter (e.g., to communicate with 3G,4G/LTE, and 5G, networks and the like), and the like. Such acommunication interface may include one or more digital and/or analogcommunication devices that permit a machine to communicate with othermachines and devices, such as remotely located components, via anetwork. The network may provide the functionality of a local areanetwork (LAN), a wireless network, a cloud computing environment (e.g.,software as a service, platform as a service, infrastructure as aservice, etc.), a client-server, a wide area network (WAN), and thelike. For example, the network may be a LAN or a WAN that may includeother systems (including additional image processing computing systemsor image-based components associated with medical imaging orradiotherapy operations).

In an example, the radiotherapy processing computing system 110 mayobtain image data 152 from the image data source 150 (e.g., MR images)for hosting on the storage device 116 and the memory 114. In yet anotherexample, the software programs may substitute functions of the patientimages such as signed distance functions or processed versions of theimages that emphasize some aspect of the image information.

In an example, the radiotherapy processing computing system 110 mayobtain or communicate image data 152 from or to image data source 150.In further examples, the treatment data source 160 receives or updatesthe planning data as a result of a treatment plan generated by thetreatment processing logic 120. The image data source 150 may alsoprovide or host the imaging data for use in the treatment processinglogic 120.

In an example, computing system 110 may communicate with treatment datasource(s) 160 and input device 148 to generate pairs of a plurality oftraining radiotherapy treatment plan information and a plurality oftraining radiotherapy treatment plan parameters; pairs of training MRand/or CBCT images and training sCT images; pairs of training MR, CT,sCT, CBCT images, segmentation and distance maps and trainingradiotherapy device control points; and pairs of training dosecomputation functions and training dose distributions.

The processing circuitry 112 may be communicatively coupled to thememory 114 and the storage device 116, and the processing circuitry 112may be configured to execute computer-executable instructions storedthereon from either the memory 114 or the storage device 116. Theprocessing circuitry 112 may execute instructions to cause medicalimages from the image data 152 to be received or obtained in memory 114and processed using the treatment processing logic 120 to generate atreatment plan. Particularly, treatment processing logic 120 receives anoptimization problem that is based on a derivative of a dose computationexpression and/or an ML model that is trained based on a derivative of adose computation expression. The treatment processing logic 120 solvesthe received optimization problem to generate one or more parameters ofa treatment plan, applies the ML model to radiotherapy treatment planinformation to estimate one or more parameters of a treatment plan,and/or applies the ML model to radiotherapy treatment plan informationto estimate an intermediate dose distribution of a dose computationfunction to simplify computation of dose distribution using theintermediate dose distribution estimate.

In addition, the processing circuitry 112 may utilize software programsto generate intermediate data such as updated parameters to be used, forexample, by a NN model, machine learning model, treatment processinglogic 120 or other aspects involved with generation of a treatment planas discussed herein. Further, such software programs may utilize thetreatment processing logic 120 to produce new or updated treatment planparameters for deployment to the treatment data source 160 and/orpresentation on output device 146, using the techniques furtherdiscussed herein. The processing circuitry 112 may subsequently thentransmit the new or updated treatment plan parameters via acommunication interface and the network to the treatment device 180,where the radiation therapy plan will be used to treat a patient withradiation via the treatment device 180, consistent with results of thetrained ML model implemented by the treatment processing logic 120(e.g., according to the processes discussed below in connection withFIG. 3).

In the examples herein, the processing circuitry 112 may executesoftware programs that invoke the treatment processing logic 120 toimplement functions of ML, deep learning, NNs, and other aspects ofartificial intelligence for treatment plan generation from an inputradiotherapy medical information (e.g., CT image, MR image, and/or sCTimage and/or dose information). For instance, the processing circuitry112 may execute software programs that train, analyze, predict,evaluate, and generate a treatment plan parameter from receivedradiotherapy medical information as discussed herein.

In an example, the image data 152 may include one or more MRI image(e.g., 2D MRI, 3D MRI, 2D streaming MRI, 4D MRI, 4D volumetric MRI, 4Dcine MRI, etc.), functional MRI images (e.g., fMRI, DCE-MRI, diffusionMRI), Computed Tomography (CT) images (e.g., 2D CT, 2D Cone beam CT, 3DCT, 3D CBCT, 4D CT, 4DCBCT), ultrasound images (e.g., 2D ultrasound, 3Dultrasound, 4D ultrasound), Positron Emission Tomography (PET) images,X-ray images, fluoroscopic images, radiotherapy portal images,Single-Photo Emission Computed Tomography (SPECT) images,computer-generated synthetic images (e.g., pseudo-CT images) and thelike. Further, the image data 152 may also include or be associated withmedical image processing data (for example, training images, groundtruth images, contoured images, and dose images). In other examples, anequivalent representation of an anatomical area may be represented innon-image formats (e.g., coordinates, mappings, etc.).

In an example, the image data 152 may be received from the imageacquisition device 170 and stored in one or more of the image datasources 150 (e.g., a Picture Archiving and Communication System (PACS),a Vendor Neutral Archive (VNA), a medical record or information system,a data warehouse, etc.). Accordingly, the image acquisition device 170may comprise a MRI imaging device, a CT imaging device, a PET imagingdevice, an ultrasound imaging device, a fluoroscopic device, a SPECTimaging device, an integrated Linear Accelerator and MRI imaging device,CBCT imaging device, or other medical imaging devices for obtaining themedical images of the patient. The image data 152 may be received andstored in any type of data or any type of format (e.g., in a DigitalImaging and Communications in Medicine (DICOM) format) that the imageacquisition device 170 and the radiotherapy processing computing system110 may use to perform operations consistent with the disclosedembodiments. Further, in some examples, the models discussed herein maybe trained to process the original image data format or a derivationthereof.

In an example, the image acquisition device 170 may be integrated withthe treatment device 180 as a single apparatus (e.g., a MRI devicecombined with a linear accelerator, also referred to as an “MRI-Linac”).Such an MRI-Linac can be used, for example, to determine a location of atarget organ or a target tumor in the patient so as to direct radiationtherapy accurately according to the radiation therapy treatment plan toa predetermined target. For instance, a radiation therapy treatment planmay provide information about a particular radiation dose to be appliedto each patient. The radiation therapy treatment plan may also includeother radiotherapy information, including control points of aradiotherapy treatment device, such as couch position, beam intensity,beam angles, dose-histogram-volume information, the number of radiationbeams to be used during therapy, the dose per beam, and the like.

The radiotherapy processing computing system 110 may communicate with anexternal database through a network to send/receive a plurality ofvarious types of data related to image processing and radiotherapyoperations. For example, an external database may include machine data(including device constraints) that provides information associated withthe treatment device 180, the image acquisition device 170, or othermachines relevant to radiotherapy or medical procedures. Machine datainformation (e.g., control points) may include radiation beam size, arcplacement, beam on and off time duration, machine parameters, segments,multi-leaf collimator (MLC) configuration, gantry speed, MRI pulsesequence, and the like. The external database may be a storage deviceand may be equipped with appropriate database administration softwareprograms. Further, such databases or data sources may include aplurality of devices or systems located either in a central or adistributed manner.

The radiotherapy processing computing system 110 can collect and obtaindata, and communicate with other systems, via a network using one ormore communication interfaces, which are communicatively coupled to theprocessing circuitry 112 and the memory 114. For instance, acommunication interface may provide communication connections betweenthe radiotherapy processing computing system 110 and radiotherapy systemcomponents (e.g., permitting the exchange of data with externaldevices). For instance, the communication interface may, in someexamples, have appropriate interfacing circuitry from an output device146 or an input device 148 to connect to the user interface 142, whichmay be a hardware keyboard, a keypad, or a touch screen through which auser may input information into the radiotherapy system.

As an example, the output device 146 may include a display device thatoutputs a representation of the user interface 142 and one or moreaspects, visualizations, or representations of the medical images, thetreatment plans, and statuses of training, generation, verification, orimplementation of such plans. The output device 146 may include one ormore display screens that display medical images, interface information,treatment planning parameters (e.g., contours, dosages, beam angles,labels, maps, etc.), treatment plans, a target, localizing a targetand/or tracking a target, or any related information to the user. Theinput device 148 connected to the user interface 142 may be a keyboard,a keypad, a touch screen or any type of device that a user may use tothe radiotherapy system 100. Alternatively, the output device 146, theinput device 148, and features of the user interface 142 may beintegrated into a single device such as a smartphone or tablet computer(e.g., Apple iPad®, Lenovo Thinkpad®, Samsung Galaxy®, etc.).

Furthermore, any and all components of the radiotherapy system may beimplemented as a virtual machine (e.g., via VMWare, Hyper-V, and thelike virtualization platforms) or independent devices. For instance, avirtual machine can be software that functions as hardware. Therefore, avirtual machine can include at least one or more virtual processors, oneor more virtual memories, and one or more virtual communicationinterfaces that together function as hardware. For example, theradiotherapy processing computing system 110, the image data sources150, or like components, may be implemented as a virtual machine orwithin a cloud-based virtualization environment.

The image acquisition device 170 can be configured to acquire one ormore images of the patient's anatomy for a region of interest (e.g., atarget organ, a target tumor or both). Each image, typically a 2D imageor slice, can include one or more parameters (e.g., a 2D slicethickness, an orientation, and a location, etc.). In an example, theimage acquisition device 170 can acquire a 2D slice in any orientation.For example, an orientation of the 2D slice can include a sagittalorientation, a coronal orientation, or an axial orientation. Theprocessing circuitry 112 can adjust one or more parameters, such as thethickness and/or orientation of the 2D slice, to include the targetorgan and/or target tumor. In an example, 2D slices can be determinedfrom information such as a 3D CBCT or CT or MRI volume. Such 2D slicescan be acquired by the image acquisition device 170 in “near real time”while a patient is undergoing radiation therapy treatment (for example,when using the treatment device 180 (with “near real time” meaningacquiring the data in at least milliseconds or less)).

The treatment processing logic 120 in the radiotherapy processingcomputing system 110 implements a ML model, which involves the use of atrained (learned) ML model. This ML model may be provided by a NNtrained as part of a NN model. One or more teacher ML models may beprovided by a different entity or at an off-site facility relative totreatment processing logic 120 and is accessible by issuing one or morequeries to the off-site facility.

Supervised machine learning (ML) algorithms or ML models or techniquescan be summarized as function approximation. Training data consisting ofinput-output pairs of some type (e.g., pairs of a plurality of trainingradiotherapy treatment plan information and a plurality of trainingradiotherapy treatment plan parameters; pairs of training MR and/or CBCTimages and training sCT images; pairs of training MR, CT, sCT, CBCTimages, segmentation and distance maps and training radiotherapy devicecontrol points; and pairs of training dose computation functions andtraining dose distributions) are acquired from, e.g., expert cliniciansor prior optimization plan solvers and a function is “trained” toapproximate this mapping. Some methods involve NNs. In these, a set ofparametrized functions A_(θ) are selected, where θ is a set ofparameters (e.g., convolution kernels and biases) that are selected byminimizing the average error over the training data. If the input-outputpairs are denoted by (x_(m), y_(m)), the function can be formalized bysolving a minimization problem such as Equation 3:

$\begin{matrix}{\min\limits_{\theta}{\sum\limits_{m = 1}^{M}{{{A_{\theta}\left( x_{m} \right)} - y_{m}}}^{2}}} & (3)\end{matrix}$

The minimization problem of Equation 3 that is used to train the networkcan be based on a loss function that includes a derivative of a dosecalculation.

Once the network has been trained (e.g., θ has been selected), thefunction A_(θ) can be applied to any new input. For example, anever-before-seen radiotherapy treatment plan information (e.g., an MRand/or CBCT image) can be fed into A_(θ), and one or more radiotherapytreatment plan parameters (e.g., an sCT image) are estimated. As anotherexample, a never-before-seen MR, CT, sCT, CBCT image, segmentation anddistance map can be fed into A_(θ), and one or more radiotherapy devicecontrol points are estimated. As another example, a never-before-seendose computation function parameters can be fed into A_(θ), and one ormore intermediate dose distributions are estimated.

Simple NNs consist of an input layer, a middle or hidden layer, and anoutput layer, each containing computational units or nodes. The hiddenlayer(s) nodes have input from all the input layer nodes and areconnected to all nodes in the output layer. Such a network is termed“fully connected.” Each node communicates a signal to the output nodedepending on a nonlinear function of the sum of its inputs. For aclassifier, the number of input layer nodes typically equals the numberof features for each of a set of objects being sorted into classes, andthe number of output layer nodes is equal to the number of classes. Anetwork is trained by presenting it with the features of objects ofknown classes and adjusting the node weights to reduce the trainingerror by an algorithm called backpropagation. Thus, the trained networkcan classify novel objects whose class is unknown.

Neural networks have the capacity to discover relationships between thedata and classes or regression values, and under certain conditions, canemulate any function y=ƒ(x) including non-linear functions. In ML, anassumption is that the training and test data are both generated by thesame data-generating process, P_(data), in which each {x_(i), y_(i)}sample is identically and independently distributed (i.i.d.). In ML, thegoals are to minimize the training error and to make the differencebetween the training and test errors as small as possible. Underfittingoccurs if the training error is too large; overfitting occurs when thetrain-test error gap is too large. Both types of performance deficiencyare related to model capacity: large capacity may fit the training datavery well but lead to overfitting, while small capacity may lead tounderfitting.

FIG. 2A illustrates an exemplary image-guided radiation therapy device232 that includes a radiation source, such as an X-ray source or alinear accelerator, a couch 246, an imaging detector 244, and aradiation therapy output 234. The radiation therapy device 232 may beconfigured to emit a radiation therapy beam 238 to provide therapy to apatient. The radiation therapy output 234 can include one or moreattenuators or collimators, such as a MLC.

As an example, a patient can be positioned in a region 242, supported bythe treatment couch 246, to receive a radiation therapy dose accordingto a radiation therapy treatment plan. The radiation therapy output 234can be mounted or attached to a gantry 236 or other mechanical support.One or more chassis motors (not shown) may rotate the gantry 236 and theradiation therapy output 234 around the couch 246 when the couch 246 isinserted into the treatment area. In an example, gantry 236 may becontinuously rotatable around couch 246 when the couch 246 is insertedinto the treatment area. In another example, gantry 236 may rotate to apredetermined position when the couch 246 is inserted into the treatmentarea. For example, the gantry 236 can be configured to rotate thetherapy output 234 around an axis (“A”). Both the couch 246 and theradiation therapy output 234 can be independently moveable to otherpositions around the patient, such as moveable in transverse direction(“T”), moveable in a lateral direction (“L”), or as rotation about oneor more other axes, such as rotation about a transverse axis (indicatedas “R”). A controller communicatively connected to one or more actuators(not shown) may control the couch 246's movements or rotations in orderto properly position the patient in or out of the radiation therapy beam238, according to a radiation therapy treatment plan. Both the couch 246and the gantry 236 are independently moveable from one another inmultiple degrees of freedom, which allows the patient to be positionedsuch that the radiation therapy beam 238 can precisely target the tumor.

The coordinate system (including axes A, T, and L) can have an originlocated at an isocenter 240. The isocenter 240 can be defined as alocation where the central axis of the radiation therapy beam 238intersects the origin of a coordinate axis, such as to deliver aprescribed radiation dose to a location on or within a patient.Alternatively, the isocenter 240 can be defined as a location where thecentral axis of the radiation therapy beam 238 intersects the patientfor various rotational positions of the radiation therapy output 234 aspositioned by the gantry 236 around the axis A.

Gantry 236 may also have an attached imaging detector 244. The imagingdetector 244 is preferably located opposite to the radiation source(output 234) and, in an example, the imaging detector 244 can be locatedwithin a field of the therapy beam 238. The imaging detector 244 can bemounted on the gantry 236, preferably opposite the radiation therapyoutput 234, so as to maintain alignment with the radiation therapy beam238. The imaging detector 244 rotates about the rotational axis as thegantry 236 rotates. In an example, the imaging detector 244 can be aflat panel detector (e.g., a direct detector or a scintillatordetector). In this manner, the imaging detector 244 can be used tomonitor the radiation therapy beam 238, or the imaging detector 244 canbe used for imaging the patient's anatomy, such as portal imaging. Thecontrol circuitry of radiation therapy device 232 may be integratedwithin the radiotherapy system 100 or remote from it.

In an illustrative example, one or more of the couch 246, the therapyoutput 234, or the gantry 236 can be automatically positioned, and thetherapy output 234 can establish the therapy beam 238 according to aspecified dose for a particular therapy delivery instance. A sequence oftherapy deliveries can be specified according to a radiation therapytreatment plan, such as using one or more different orientations orlocations of the gantry 236, couch 246, or therapy output 234. Thetherapy deliveries can occur sequentially but can intersect in a desiredtherapy locus on or within the patient, such as at the isocenter 240. Aprescribed cumulative dose of radiation therapy can thereby be deliveredto the therapy locus while damage to tissue nearby the therapy locus canbe reduced or avoided.

Thus, FIG. 2A specifically illustrates an example of a radiation therapydevice 232 operable to provide radiotherapy treatment to a patientconsistent with or according to a radiotherapy treatment plan, with aconfiguration where a radiation therapy output can be rotated around acentral axis (e.g., an axis “A”). Other radiation therapy outputconfigurations can be used. For example, a radiation therapy output canbe mounted to a robotic arm or manipulator having multiple degrees offreedom. In yet another example, the therapy output can be fixed, suchas located in a region laterally separated from the patient, and aplatform supporting the patient can be used to align a radiation therapyisocenter with a specified target locus within the patient. In anotherexample, a radiation therapy device can be a combination of a linearaccelerator and an image acquisition device. In some examples, the imageacquisition device may be an MRI, an X-ray, a CT, a CBCT, a spiral CT, aPET, a SPECT, an optical tomography, a fluorescence imaging, ultrasoundimaging, or radiotherapy portal imaging device, and the like, as wouldbe recognized by one of ordinary skill in the art.

FIG. 2B illustrates a radiotherapy device 130, a Gamma Knife in whichthe present disclosure can be used. A patient 202 may wear a coordinateframe 220 to keep stable the patient's body part (e.g. the head)undergoing surgery or radiotherapy. Coordinate frame 220 and a patientpositioning system 222 may establish a spatial coordinate system, whichmay be used while imaging a patient or during radiation surgery.Radiotherapy device 130 may include a protective housing 214 to enclosea plurality of radiation sources 212 for generation of radiation beams(e.g. beamlets) through beam channels 216. The plurality of beams may beconfigured to focus on an isocenter 218 from different locations. Whileeach individual radiation beam may have relatively low intensity,isocenter 218 may receive a relatively high level of radiation whenmultiple doses from different radiation beams accumulate at isocenter218. In certain embodiments, isocenter 218 may correspond to a targetunder surgery or treatment, such as a tumor.

FIG. 3 illustrates an exemplary data flow for training and use ofmachine learning model(s) based on a derivative of a dose, according tosome examples of the disclosure. The data flow includes training input310, ML model(s) (technique(s)) training 330, and model(s) usage 350.

Training input 310 includes model parameters 312 and training data 320which may include paired training data sets 322 (e.g., input-outputtraining pairs) and constraints 326. Model parameters 312 stores orprovides the parameters or coefficients of corresponding ones of machinelearning models Â_(θ). During training, these parameters 312 are adaptedbased on the input-output training pairs of the training data sets 322.After the parameters 312 are adapted (after training), the parametersare used by trained treatment models 360 to implement the respective oneof the trained machine learning models Â_(θ) on a new set of data 370.

Training data 320 includes constraints 326 which may define the physicalconstraints of a given radiotherapy device. The paired training datasets 322 may include sets of input-output pairs, such as a pairs of aplurality of training radiotherapy treatment plan information and aplurality of training radiotherapy treatment plan parameters; pairs oftraining MR and/or CBCT images and training sCT images; pairs oftraining MR, CT, sCT, CBCT images, segmentation and distance maps andtraining radiotherapy device control points; and pairs of training dosecomputation functions and training dose distributions. Some componentsof training input 310 may be stored separately at a different off-sitefacility or facilities than other components.

Machine learning model(s) training 330 trains one or more machinelearning techniques Â_(θ) based on the sets of input-output pairs ofpaired training data sets 322. For example, the model training 330 maytrain the ML model parameters 312 by minimizing a first loss functionbased on one or more derivatives of a dose computation. Particularly,the ML model can be applied to radiotherapy treatment plan informationto estimate one or more radiotherapy treatment plan parameters. In someimplementations, a derivative of a loss function is computed based onthe one or more radiotherapy treatment plan parameters and parameters ofthe ML model are updated based on the computed derivative of the lossfunction. In some implementations, the radiotherapy treatment planparameters are applied to a dose computation function, such as onedefined by Equation 2, to compute a first dose. A gradient or derivativeof a loss function to which the computed first dose is applied iscomputed and parameters of the ML model are updated based on thecomputed gradient or derivative. In some implementations supervisedtraining approaches are used in which a second dose is computed based ontraining radiotherapy treatment plan parameters corresponding to thereceived radiotherapy treatment plan. In such cases, both dosecomputations are applied to the loss function for which the gradient orderivative of the loss function is computed and parameters of the MLmodel are updated based on the computed gradient or derivative. Inunsupervised ML model training, only the first dose is applied to theloss function for which the gradient or derivative is computed andevaluated against a metric to update parameters of the ML model.

The result of minimizing the loss function for multiple sets of trainingdata trains, adapts, or optimizes the model parameters 312 of thecorresponding ML models. In this way, the ML model is trained toestablish a relationship between a plurality of training radiotherapytreatment plan information and a plurality of training radiotherapytreatment plan parameters.

The ML model is trained in one implementation according to supervisedlearning techniques to estimate radiotherapy treatment plan parametersfrom radiotherapy treatment plan information. Supervised learningtechniques assume that x_(ƒ)*=arg min_(x) ƒ(x) is known from previouslycomputing radiotherapy treatment plan parameters corresponding toradiotherapy treatment plan information. In such cases, to train the MLmodel Λ_(θ), a plurality of training radiotherapy treatment planinformation are retrieved together with their corresponding trainingradiotherapy treatment plan parameters. The ML model is applied to afirst batch of training radiotherapy treatment plan information toestimate a given set of radiotherapy treatment plan parameters. Thebatch of the training radiotherapy treatment plan information can beused to train the ML model with the same parameters of the ML model andmay range from one training radiotherapy treatment plan information toall of the training radiotherapy treatment plan information. In someimplementations, the output or result of the ML model is used to computea first dose or derivative of a first dose (e.g., by computing aderivative, such as using an automatic differentiation process, ofEquation 2 that includes the output or result of the ML model).Additionally, the radiotherapy treatment plan parameters correspondingto the batch of radiotherapy treatment plan information is used tocompute a second dose or derivative of a second dose in a similarmanner. The first dose (or derivative of the first dose) and the seconddose (or derivative of the second dose) are applied to a loss functionand a gradient or derivative of the loss function with the applied dosesis computed. Based on the gradient or derivative of the loss function,updated parameters for the ML model are computed. In someimplementations, a derivative of the loss function is computed based onthe radiotherapy treatment plan parameters and parameters of the MLmodel are updated based on the computed derivative of the loss function.For example, a computation of dL/dD (derivative of the loss functionwith respect to a dose calculation) is performed given the current setof estimated radiotherapy treatment plan parameters and correspondingset of radiotherapy treatment plan parameters. Another computation ofdD/d(ML model parameters) (derivative of dose calculation with respectto ML model parameters) is performed and the ML model parameters areupdated based on the current ML model parameters and the computation ofdD/d(ML model parameters). The ML model is then applied with the updatedparameters to a second batch of training radiotherapy treatment planinformation to again estimate a given set of parameters of aradiotherapy treatment plan to compute doses and apply the doses to aloss function. Parameters of the ML model are again updated anditerations of this training process continue for a specified number ofiterations or epochs or until a given convergence criteria has been met.

The ML model is trained in one implementation according to supervisedlearning techniques to estimate an sCT image from one or more medicalimages (e.g., an MR image, CT image, and/or a CBCT image). In suchcases, to train the ML model Λ_(θ), a plurality of training medicalimages are retrieved together with their corresponding training sCTimages and/or training CT images. The ML model is applied to a firstbatch of training medical images to estimate a given set of sCT images.The batch of the training medical images can be used to train the MLmodel with the same parameters of the ML model and may range from onetraining medical image to all of the training medical images. In someimplementations, the output or result of the ML model is used to computea first dose or derivative of a first dose (e.g., by computing aderivative, such as using an automatic differentiation process, ofEquation 2 that includes the output or result of the ML model).Additionally, the sCT images corresponding to the batch of medicalimages is used to compute a second dose or derivative of a second dosein a similar manner. The first dose (or derivative of the first dose)and the second dose (or derivative of the second dose) are applied to aloss function and a gradient or derivative of the loss function with theapplied doses is computed. Based on the gradient or derivative of theloss function, updated parameters for the ML model are computed. In someimplementations, a derivative of the loss function is computed based onthe sCT image and parameters of the ML model are updated based on thecomputed derivative of the loss function. For example, a computation ofdL/dD (derivative of the loss function with respect to a dosecalculation) is performed given the current set of estimated sCT imagesand corresponding set of training sCT or CT images. Another computationof dD/d(ML model parameters) (derivative of dose calculation withrespect to ML model parameters) is performed and the ML model parametersare updated based on the current ML model parameters and the computationof dD/d(ML model parameters). The ML model is then applied with theupdated parameters to a second batch of training medical images to againestimate a given set of sCT images to compute doses and apply the dosesto a loss function. Parameters of the ML model are again updated anditerations of this training process continue for a specified number ofiterations or epochs or until a given convergence criteria has been met.

The ML model is trained in one implementation according to supervisedlearning techniques to estimate a dose distribution or radiotherapytreatment plan from a CT image, a segmentation map and/or a distancemap. In such cases, to train the ML model Λ_(θ), a plurality of trainingCT image, a segmentation map and/or a distance map are retrievedtogether with their corresponding training dose distribution orradiotherapy treatment plan. The ML model is applied to a first batch oftraining CT image, a segmentation map and/or a distance map to estimatea given set of dose distribution or radiotherapy treatment plan. Thebatch of the training CT image, a segmentation map and/or a distance mapcan be used to train the ML model with the same parameters of the MLmodel and may range from one training CT image, a segmentation mapand/or a distance map to all of the training CT images, a segmentationmaps and/or a distance maps. In some implementations, the output orresult of the ML model is used to compute a first dose or derivative ofa first dose (e.g., by computing a derivative, such as using anautomatic differentiation process, of Equation 2 that includes theoutput or result of the ML model). Additionally, the dose distributionor radiotherapy treatment plan corresponding to the batch of a CT image,a segmentation map and/or a distance map is used to compute a seconddose or derivative of a second dose in a similar manner. The first dose(or derivative of the first dose) and the second dose (or derivative ofthe second dose) are applied to a loss function and a gradient orderivative of the loss function with the applied doses is computed.Based on the gradient or derivative of the loss function, updatedparameters for the ML model are computed. In some implementations, aderivative of the loss function is computed based on the dosedistribution or radiotherapy treatment plan and parameters of the MLmodel are updated based on the computed derivative of the loss function.For example, a computation of dL/dD (derivative of the loss functionwith respect to a dose calculation) is performed given the current setof estimated dose distribution or radiotherapy treatment plan andcorresponding set of training dose distribution or radiotherapytreatment plan. Another computation of dD/d(ML model parameters)(derivative of dose calculation with respect to ML model parameters) isperformed and the ML model parameters are updated based on the currentML model parameters and the computation of dD/d(ML model parameters).The ML model is then applied with the updated parameters to a secondbatch of training a CT image, a segmentation map and/or a distance mapto again estimate a given set of dose distribution or radiotherapytreatment plan to compute doses and apply the doses to a loss function.Parameters of the ML model are again updated and iterations of thistraining process continue for a specified number of iterations or epochsor until a given convergence criteria has been met.

The ML model is trained in one implementation according to supervisedlearning techniques to estimate a second dose calculation from a firstdose calculation. In such cases, to train the ML model Λ_(θ), aplurality of training first dose calculations are retrieved togetherwith their corresponding training second dose calculations. The ML modelis applied to a first batch of training first dose calculations toestimate a given set of second dose calculations. The batch of thetraining first dose calculation can be used to train the ML model withthe same parameters of the ML model and may range from one trainingfirst dose calculation to all of the training first dose calculations.The estimated second dose calculation and the retrieved training seconddose calculation, corresponding to the training first dose calculation,are applied to a loss function and a gradient or derivative of the lossfunction with the applied dose calculations is computed. Based on thegradient or derivative of the loss function, updated parameters for theML model are computed. The ML model is then applied with the updatedparameters to a second batch of training first dose calculations toagain estimate a given set of second dose calculations and apply thedose calculations to a loss function. Parameters of the ML model areagain updated and iterations of this training process continue for aspecified number of iterations or epochs or until a given convergencecriteria has been met.

The ML model trained in one implementation according to unsupervisedlearning techniques, wherein the true radiotherapy treatment parameterscorresponding to radiotherapy treatment plan information are not used(regardless of whether they are known or not). In such cases, to trainthe ML model Λ_(θ), a plurality of training radiotherapy treatment planinformation for other patients (and/or that include syntheticallygenerated radiotherapy treatment plan information) are retrieved. The MLmodel is applied to a first batch of the training radiotherapy treatmentplan information to estimate a given set of radiotherapy treatment planparameters (e.g., sCT images, a radiotherapy treatment plan, a dosecalculation, a dose distribution). The batch of the trainingradiotherapy treatment plan information can be used to train the MLmodel with the same parameters of the ML model and may range from onetraining radiotherapy treatment plan information to all of the trainingradiotherapy treatment plan information. The output or result of the MLmodel is used to compute a dose or derivative of a dose based onEquation 2 which is evaluated using a loss function to obtain feedbackon the loss/utility of the current iteration. Based on a gradient orderivative of this loss function, updated parameters for the ML modelare computed. The ML model is then applied with the updated parametersto a second batch of training radiotherapy treatment plan information toagain estimate a given set of radiotherapy treatment plan parameters.Parameters of the ML model are again updated and iterations of thistraining process continue for a specified number of iterations or epochsor until a given convergence criteria has been met.

In some embodiments, the derivative of the dose (the first dose or thesecond dose) includes a first order derivative. In some implementations,the first order derivative is not a constant value.

Specifically, the ML model is trained in a supervised or unsupervisedmanner based on the loss function such that a derivative of a dosecomputed based on a set of radiotherapy treatment plan parametersestimated by the ML model for a given radiotherapy treatment planinformation satisfies a stopping criteria after a specified fixed numberof iterations. Specifically, the ML model is trained until a stoppingcriteria is met (e.g., a maximum number of iterations has been reached,a decrease in objective value is achieved, a step length is met, etc.)or when a dose or derivative of a dose computed based on an output ofthe ML model is within a specified threshold error. In this way, thistrained ML model can be applied to a new radiotherapy treatment planinformation to estimate one or more radiotherapy treatment planparameters.

After the machine learning model Â_(θ) (sometimes referred to as Λ_(θ))is trained, new data 370, including one or more patient input parameters(e.g., radiotherapy treatment plan information, such as a medicalimage), may be received. The trained machine learning technique Â_(θ)may be applied to the new data 370 to generate generated results 380including one or more estimated parameters (e.g., an sCT image) of theradiotherapy treatment plan.

In some embodiments, an estimated sCT image is used to compute aderivative of a dose expression. The purpose of the sCT image is oftento check whether the dose deviations are acceptable if the treatmentplanned beforehand is delivered to the current patient anatomy andposition. An important criteria for the sCT image is thus that itresults in an accurate description of the dose distribution that wouldresult from delivering the treatment. Typical systems fail to tune thesCT image generation explicitly to achieve this objective of an accuratedescription of the dose distribution. Some approaches have settled for aproxy where the sCT image is tuned to have image intensities (HounsfieldUnits) that are as similar as possible to a corresponding CT image.

As such, in some implementations, differentiable dose calculations areused to improve sCT image generation. To do so, a ML model is trained totake an MR image as the input x and output y as a sCT image (a volumewith values given in Hounsfield Units, hence a geometry parameter ofEquation 2), where the training objective is to minimize the dosedeviations (in L2 norm) on a training database of old treatment plans.That means the ML model is trained in a supervised machine learningalgorithm ƒ_(θ)(x) by selecting the parameters θ to minimize a dosebased empirical loss L given by Equation 4:

L(ƒ_(θ)(x),y)=E _(Ψ,Ω)[∥D(Ψ,ƒ_(θ)(x))−D(Ψ,y)∥₂ ²]  (4)

If the dose calculation is differentiable, the ML model can be trainedwith this loss in and end-to-end fashion and the chain rule can be usedto compute the derivate of the loss according to Equation 5:

$\begin{matrix}{\frac{dL}{d\;\theta} = {{\frac{dL}{dD}\left( {{\frac{\partial D}{\partial\Psi}\frac{d\;\Psi}{d\;\theta}} + {\frac{\partial D}{\partial\Omega}\frac{d\;\Omega}{d\;\theta}}} \right)} = {\frac{dL}{dD}\frac{\partial D}{\partial f}\frac{df}{d\;\theta}}}} & (5)\end{matrix}$

where the second equality holds in this example because

$\frac{d\;\Psi}{d\;\theta} = 0$

Ψ doesn't depend on θ) and Ω=ƒ_(θ)(x). The training results in a fixedset of parameters θ* for the model. When presented with a previouslyunseen MR image {circumflex over (x)}, a synthetic CT image can begenerated by applying the learned function ƒ_(θ*)({circumflex over(x)}). A detailed diagram of this example is shown and described belowin connection with FIG. 7.

In some embodiments, the ML model that is trained based on a derivativeof a dose expression can be used to perform automatic treatmentplanning. In automatic treatment planning, the aim is to automaticallygenerate treatments plans based only on inputs such as medical imagesand delineated structures (structure sets). The most successfulstrategies try to replicate the treatment intent, typically expressed interms of the dose distribution or some quantity derived from it, usingsome element of learning based on previous treatment plans. However,learning the dose distribution is only a halfway-solution, because todeliver the treatment, a machine configuration that achieves thetreatment plan is needed. The step of finding the machine configurationscan be considered as a form of inverse problem, which is typicallyapproached by formulating and solving an optimization problem. So far,the performance degradation that results when moving from prediction torealizable plan has mostly been ignored.

As such, according to some embodiments, by explicitly incorporatingknowledge of the forward model, e.g., the dose calculation, it becomespossible to efficiently use supervised learning to train a ML model thatmaps directly from medical images to a machine configuration (or controlpoints).

Specifically, to train such a ML model, let x be inputs in the form of aCT scan and a collection of distance maps from a number of differentstructures, and let y be machine parameters in the form of a collectionof apertures and irradiation angles. The dose calculation uses both theCT images and the machine parameters to simulate the resulting dosedistribution, e.g., D={circumflex over (D)}(x, y). A neural network canbe used with parameters 9 to describe the mapping ƒ_(θ): X→Y from theinputs to the machine parameters.

In the most straightforward setting, a loss function can be used thatdirectly compares the deviation in the space of machine parameters e.g.,L(ƒ_(θ)(x), y)=∥ƒ_(θ)(x)−y∥₂ ². In certain cases, the comparison isperformed in the space of dose distributions according to Equation 6:

L(ƒ_(θ)(x),y)=∥D(x,y)−D(x,ƒ _(θ)(x))∥₂ ²  (6)

Training such an ML model can be performed in a similar manner as shownand described in FIG. 8.

In some embodiments, an optimization problem can be solved usingparameters that are based on a derivative of a dose expression to selectirradiation directions. The use of differential dose calculations issimilar for beam orientation selection for linear accelerators as it isfor isocenter selection for Gamma Knife treatments. A specific examplefocuses on Gamma Knife and is discussed in connection with FIG. 9.

Leksell Gamma Knife (LGK) is a dedicated system for intracranialstereotactic radiosurgery. The radiation is collimated to create a focuswhere the radiation from every source converges. At the focus, both theradiation intensity and its gradient become very large. This makes itpossible to deliver high radiation doses with minimal damage tosurrounding healthy tissue. There may be two ways of tailoring theradiation dose according to the shape and size of the target. First, thepatient can be precisely moved (robotically) in relation to the focus,effectively placing the focus in different isocenters. Second, theradiation sources are arranged in eight, independently controlled,sectors. Each sector can be in one of four different collimator states:the 4, 8, or 16 mm or in the beam-off state. For each isocenter positionand collimator configuration (e.g., collimator size for each sector),the irradiation time can be specified. This composition is oftenreferred to as a shot.

The large number of degrees of freedom allows sculpting of the dosedistribution in unparalleled ways. At the same time, however, it isinfeasible to explore them all by means of manual planning. Thus, aninverse planning method is utilized to make the full potential of LGKclinically accessible. Inverse planning methods only require the user tospecify what objectives to strive for, and then use mathematicaloptimization to search for the best possible treatment plan according tothese objectives. Typically, a set of isocenters is generated by meansof a heuristic, geometric, algorithm. For these isocenters, the doserate kernel is precomputed and its shape fixed, so that even if anisocenter is moved as part of the optimization, the corresponding dosekernel is simply moved in the same way. In other words, such typicaltechniques do not take tissue inhomogeneities into account.

In reality, the total dose is given by the superposition of the dosesgiven with all collimator sizes, from all sectors at every isocenter,but for the sake of simplicity only the case with just a singlecollimator, sector and isocenter (the multidimensional version justconsists of replacing the multiplication with a matrix-vectormultiplication) is discussed and represented by Equation 7:

$\begin{matrix}{{D\left( {r,r^{\prime},\ t} \right)} = {{\psi\left( {r,r^{\prime}} \right)}t}} & (7)\end{matrix}$

where ψ(r, r′) is the dose rate at position r due to an isocenter atposition r′, and t is the irradiation time.

In this example, the loss function (also known as objective function orutility function) of the inverse planning problem would be a function ofthe parameters θ={r′, t} (radiation parameters), for instance defined byEquation 8:

L(r′,t)=w _(T)∫_(V) _(T) max({circumflex over (D)}(r)−D(r,r′,t),0)dr+w_(O)∫_(V) _(O) max(D(r,r′t)−{circumflex over (D)}(r),0)dr+|t|,  (8)

where w_(T) and w_(O) are scalar weight factors that control theimportance of giving higher dose than {circumflex over (D)} to thetarget volume V_(T) and reducing the dose below {circumflex over (D)} inan organ at risk volume V_(O). The |t|-term expresses the desire to keeptreatment times short. If the physical constraint is incorporated thatthe irradiation time is necessarily nonnegative, t≥0, the inverseplanning problem can be expressed as the nonlinear, bound constrained,optimization problem of Equation 9:

$\begin{matrix}{{\underset{r^{\prime},t}{minimize}\;{L\left( {r^{\prime},t} \right)}}{{{subject}\mspace{14mu}{to}\mspace{14mu} t} \geq 0}} & (9)\end{matrix}$

This type of optimization problem can be solved using e.g., an iterativegradient-based scheme such as projected (sub)gradient descent:

θ^((k+1))=proj_(t≥0)(θ^((k)) −ηg ^((k))),

where proj_(t≥0) is the projection onto the feasible set t≥0, η∈

is a stepsize and g^((k)) is any subgradient at θ^((k)). The reasonsubgradients are used in this case is because the particular lossfunction in Equation (9) is non-differentiable when D={circumflex over(D)}. The gradient of the loss with respect to the parameters can beevaluated using the chain rule according to Equation 10:

$\begin{matrix}{{\frac{dL}{d\; r^{\prime}} = {{\frac{dL}{dD}\frac{\partial D}{\partial r^{\prime}}} = {\frac{dL}{dD}\left( {\frac{\partial\psi}{\partial r^{\prime}} \cdot t} \right)}}},{\frac{dL}{dt} = {{\frac{dL}{dD}\frac{\partial D}{\partial t}} = {\frac{dL}{dD} \cdot \psi}}},} & (10)\end{matrix}$

The gradient of the dose kernel with respect to the isocenter positioncan be evaluated numerically or by means of automatic differentiation.

A variation of the above is to introduce auxiliary variables s(r) toformulate a smooth optimization problem that is equivalent to Equation(9) but defined in terms of the larger set of variables (r′ t, s). Inthis modified problem, the loss is expressed by Equation 11:

$\begin{matrix}{{{L\left( {r^{\prime},\ t,\ s} \right)} = {{w_{T}{\int_{V_{T}}{{s(r)}{dr}}}} + {w_{o}{\int_{V_{o}}{{s(r)}{dr}}}} + {t}}},} & (10)\end{matrix}$

and the additional set of constraints are defined as follows:

$\begin{matrix}{0 \leq {s(r)}} & {{r \in V_{T}},} \\{{{\overset{\hat{}}{D}(r)} - {D\left( {r,\ r^{\prime},\ t} \right)}} \leq {s(r)}} & {{r \in V_{T}},} \\{0 \leq {s(r)}} & {{r \in V_{O}},} \\{{{D\left( {r,\ r^{\prime},\ t} \right)} - {\overset{\hat{}}{D}(r)}} \leq {s(r)}} & {r \in {V_{O}.}}\end{matrix}$

This optimization problem of Equation 10 can be solved usinghigher-order optimization methods, e.g., a Newton method, which makesuses of the second order derivate (Hessian) or using a (quasi-Newton)solver, e.g., sequential quadratic programming (SQP) or an interiorpoint method, that is known to work well even with the type of nonlinearconstraints of this problem.

FIG. 4 is a flowchart illustrating example operations of the treatmentprocessing logic 120 in performing process 400, according to exampleembodiments. The process 400 may be embodied in computer-readableinstructions for execution by one or more processors such that theoperations of the process 400 may be performed in part or in whole bythe functional components of the treatment processing logic 120;accordingly, the process 400 is described below by way of example withreference thereto. However, in other embodiments, at least some of theoperations of the process 400 may be deployed on various other hardwareconfigurations. The process 400 is therefore not intended to be limitedto the treatment processing logic 120 and can be implemented in whole,or in part, by any other component. Some or all of the operations ofprocess 400 can be in parallel, out of order, or entirely omitted.

At operation 410, treatment processing logic 120 receives training data.For example, treatment processing logic 120 receives pairs of aplurality of training radiotherapy treatment plan information and aplurality of training radiotherapy treatment plan parameters; pairs oftraining MR and/or CBCT images and training sCT images; pairs oftraining MR, CT, sCT, CBCT images, segmentation and distance maps andtraining radiotherapy device control points; and pairs of training dosecomputation functions and training dose distributions.

At operation 420, treatment processing logic 120 receives constraintsfor training.

At operation 430, treatment processing logic 120 performs training ofthe model. For example, treatment processing logic 120 may train the MLmodel parameters 312 (FIG. 3) by minimizing a gradient or derivative ofa loss function to which one or more dose computations have been appliedthat have been estimated based on one or more training radiotherapytreatment plan information and the corresponding training radiotherapytreatment plan parameters. In this way, the ML model is trained toestablish a relationship between radiotherapy treatment plan informationand one or more radiotherapy treatment plan parameters. The training canbe performed in a supervised or unsupervised manner.

At operation 440, treatment processing logic 120 outputs the trainedmodel. For example, the trained model can be output and stored in amemory or parameters of the model can be presented on a display deviceto a clinician.

At operation 450, treatment processing logic 120 utilizes the trainedmodel to generate results. For example, after each of the machinelearning models Â_(θ) (sometimes referred to as Λ_(θ)) is trained, newdata 370, including one or more patient input parameters (e.g.,radiotherapy treatment plan information), may be received. The trainedmachine learning technique Â_(θ) may be applied to the new data 370 togenerate generated results 380 including one or more estimatedradiotherapy treatment plan parameters.

FIG. 5 is a flowchart illustrating example operations of the treatmentprocessing logic 120 in performing process 500, according to exampleembodiments. The process 500 may be embodied in computer-readableinstructions for execution by one or more processors such that theoperations of the process 500 may be performed in part or in whole bythe functional components of the treatment processing logic 120;accordingly, the process 500 is described below by way of example withreference thereto. However, in other embodiments, at least some of theoperations of the process 500 may be deployed on various other hardwareconfigurations. The process 500 is therefore not intended to be limitedto the treatment processing logic 120 and can be implemented in whole,or in part, by any other component. Some or all of the operations ofprocess 500 can be in parallel, out of order, or entirely omitted.

At operation 510, treatment processing logic 120 receives a radiotherapytreatment plan information.

At operation 520, treatment processing logic 120 processes theradiotherapy treatment plan information to estimate one or moreradiotherapy treatment plan parameters based on a process that dependson the output of a subprocess that estimates a derivative of a dosecalculation.

At operation 530, treatment processing logic 120 generates aradiotherapy treatment plan using the estimated one or more radiotherapytreatment plan parameters.

FIG. 6 is a flowchart illustrating example operations of the treatmentprocessing logic 120 in performing a process 600, according to exampleembodiments. The process 600 may be embodied in computer-readableinstructions for execution by one or more processors such that theoperations of the process 600 may be performed in part or in whole bythe functional components of the treatment processing logic 120;accordingly, the process 600 is described below by way of example withreference thereto. However, in other embodiments, at least some of theoperations of the process 600 may be deployed on various other hardwareconfigurations. The process 600 is therefore not intended to be limitedto the treatment processing logic 120 and can be implemented in whole,or in part, by any other component. Some or all of the operations ofprocess 600 can be in parallel, out of order, or entirely omitted.

At operation 610, treatment processing logic 120 receives a plurality oftraining radiotherapy treatment plan information and a plurality oftraining radiotherapy treatment plan parameters.

At operation 620, treatment processing logic 120 trains the machinelearning model to generate an estimate of the radiotherapy treatmentplan parameters by establishing a relationship between the plurality oftraining radiotherapy treatment plan information and the plurality oftraining radiotherapy treatment plan parameters based on dosecomputation.

FIG. 7 is a flowchart illustrating example operations of the treatmentprocessing logic 120 in performing a process 700, according to exampleembodiments. The process 700 may be embodied in computer-readableinstructions for execution by one or more processors such that theoperations of the process 700 may be performed in part or in whole bythe functional components of the treatment processing logic 120;accordingly, the process 700 is described below by way of example withreference thereto. However, in other embodiments, at least some of theoperations of the process 700 may be deployed on various other hardwareconfigurations. The process 700 is therefore not intended to be limitedto the treatment processing logic 120 and can be implemented in whole,or in part, by any other component. Some or all of the operations ofprocess 700 can be in parallel, out of order, or entirely omitted.

At operation 710, treatment processing logic 120 accesses a database ofprior radiotherapy treatment plans.

At operation 712, treatment processing logic 120 defines a parameterizedfunction (e.g., an ML model) for generating an sCT image from a CT,CBCT, and/or MRI image.

At operation 714, treatment processing logic 120 define a derivative ofdose based loss function. For example, the treatment processing logic120 can obtain the loss function defined by Equation 4.

At operation 720, treatment processing logic 120 trains the parametrizedfunction. For example, the treatment processing logic 120 operates onbatches of training CT, CBCT, and/or MRI images and their correspondingtraining sCT images to train the ML model based on the loss functiondefined by Equation 4.

At operation 722, treatment processing logic 120 begins training theparameterized function. For example, the treatment processing logic 120obtains a first batch of training CT, CBCT, and/or MRI images and theircorresponding training sCT images. The treatment processing logic 120applies the ML model to the first batch of training CT, CBCT, and/or MRIimages to estimate an intermediate sCT image. The treatment processinglogic 120 computes a derivative or gradient of a first dose based on theintermediate sCT image and a second dose based on the training sCTimage. The first and second dose derivatives or gradients are applied tothe loss function of Equation 4.

At operation 724, treatment processing logic 120 determines if stoppingcriteria for training the parameterized function has been met. If thestopping criteria has been met, the treatment processing logic 120proceeds to end training and otherwise the treatment processing logic120 proceeds to operation 726.

At operation 726, treatment processing logic 120 computes a gradient orderivative of loss with respect to parameters of the parameterizedfunction.

At operation 727, treatment processing logic 120 updates parameters ofthe parameterized function based on the gradient or derivative of theloss.

At operation 728, treatment processing logic 120 evaluates if stoppingcriteria has been met. If not, another batch of training data isaccessed and another iteration of training the ML model is performed.

At operation 730, treatment processing logic 120 outputs the trained MLmodel.

At operation 740, treatment processing logic 120 applies the trained MLmodel to a new MRI, CBCT, and/or CT image to generate an sCT image.

FIG. 8 is a flowchart illustrating example operations of the treatmentprocessing logic 120 in performing a process 800, according to exampleembodiments. The process 800 may be embodied in computer-readableinstructions for execution by one or more processors such that theoperations of the process 800 may be performed in part or in whole bythe functional components of the treatment processing logic 120;accordingly, the process 800 is described below by way of example withreference thereto. However, in other embodiments, at least some of theoperations of the process 800 may be deployed on various other hardwareconfigurations. The process 800 is therefore not intended to be limitedto the treatment processing logic 120 and can be implemented in whole,or in part, by any other component. Some or all of the operations ofprocess 800 can be in parallel, out of order, or entirely omitted.

At operation 810, treatment processing logic 120 accesses a database ofprior radiotherapy treatment plans.

At operation 812, treatment processing logic 120 defines parameterizedfunction for generating a radiotherapy treatment plan from a CT and/orMRI image and a segmentation map and/or distance map.

At operation 814, treatment processing logic 120 define derivative ofdose based loss function. For example, the treatment processing logic120 can obtain the loss function defined by Equation 6.

At operation 820, treatment processing logic 120 trains the parametrizedfunction. For example, the treatment processing logic 120 operates onbatches of training CT and/or MRI images and segmentation maps and/ordistance maps and their corresponding training radiotherapy treatmentplans to train the ML model based on the loss function defined byEquation 6.

At operation 822, treatment processing logic 120 begins training theparameterized function. For example, the treatment processing logic 120obtains a first batch of training CT and/or MRI images and segmentationmaps and/or distance maps and their corresponding training radiotherapytreatment plans. The treatment processing logic 120 applies the ML modelto the first batch of training CT and/or MRI images and segmentationmaps and/or distance maps to estimate an intermediate radiotherapytreatment plan. The treatment processing logic 120 computes a derivativeor gradient of a first dose based on the intermediate radiotherapytreatment plan and a second dose based on the training radiotherapytreatment plan. The first and second dose derivatives or gradients areapplied to the loss function of Equation 6.

At operation 824, treatment processing logic 120 determines if stoppingcriteria for training the parameterized function has been met. If thestopping criteria has been met, the treatment processing logic 120proceeds to end training and otherwise the treatment processing logic120 proceeds to operation 826.

At operation 826, treatment processing logic 120 computes a gradient orderivative of loss with respect to parameters of the parameterizedfunction.

At operation 827, treatment processing logic 120 updates parameters ofthe parameterized function based on the gradient or derivative of theloss.

At operation 828, treatment processing logic 120 evaluates if stoppingcriteria has been met. If not, another batch of training data isaccessed and another iteration of training the ML model is performed.

At operation 830, treatment processing logic 120 outputs the trained MLmodel.

At operation 840, treatment processing logic 120 applies the trained MLmodel to a new MRI and/or CT image and segmentation map and/or distancemap to generate a radiotherapy treatment plan.

FIG. 9 is a flowchart illustrating example operations of the treatmentprocessing logic 120 in performing a process 900, according to exampleembodiments. The process 900 may be embodied in computer-readableinstructions for execution by one or more processors such that theoperations of the process 900 may be performed in part or in whole bythe functional components of the treatment processing logic 120;accordingly, the process 900 is described below by way of example withreference thereto. However, in other embodiments, at least some of theoperations of the process 900 may be deployed on various other hardwareconfigurations. The process 900 is therefore not intended to be limitedto the treatment processing logic 120 and can be implemented in whole,or in part, by any other component. Some or all of the operations ofprocess 900 can be in parallel, out of order, or entirely omitted.

At operation 910, treatment processing logic 120 accesses a database ofan initial set of isocentre locations.

At operation 912, treatment processing logic 120 initializes acollimator configuration for each sector of each isocenter.

At operation 914, treatment processing logic 120 defines a loss functionand constraints including a derivative of a dose expression for anoptimization problem. For example, the treatment processing logic 120obtains the loss function of Equation 8 or 10.

At operation 920, treatment processing logic 120 optimizes theoptimization problem.

At operation 922, treatment processing logic 120 begins optimization ofthe optimization problem. For example, the treatment processing logic120 begins solving the optimization problem using iterative gradientdecent.

At operation 924, treatment processing logic 120 determines if stoppingcriteria for optimizing the optimization problem has been met. If thestopping criteria has been met, the treatment processing logic 120proceeds to end optimization and otherwise the treatment processinglogic 120 proceeds to operation 926.

At operation 926, treatment processing logic 120 computes a gradient orderivative of loss and/or constraints of the optimization problem.

At operation 927, treatment processing logic 120 updates parameters ofthe optimization problem based on the gradient or derivative.

At operation 928, treatment processing logic 120 evaluates if stoppingcriteria has been met.

At operation 930, treatment processing logic 120 outputs the selectedisocenters and other treatment plan parameters.

At operation 940, treatment processing logic 120 determines if thetreatment plan quality is acceptable (e.g., satisfies a qualitythreshold). If the quality is acceptable, the treatment processing logic120 proceeds to output the treatment plan at operation 960 and otherwisethe treatment processing logic 120 proceeds to operation 950.

At operation 950, treatment processing logic 120 performs inverseplanning with isocenters being fixed.

As previously discussed, respective electronic computing systems ordevices may implement one or more of the methods or functionaloperations as discussed herein. In one or more embodiments, theradiotherapy processing computing system 110 may be configured, adapted,or used to control or operate the image-guided radiation therapy device232, perform or implement the training or prediction operations fromFIG. 3, operate the trained treatment model 360, perform or implementthe operations of the flowcharts for processes 400-900, or perform anyone or more of the other methodologies discussed herein (e.g., as partof treatment processing logic 120). In various embodiments, suchelectronic computing systems or devices operates as a standalone deviceor may be connected (e.g., networked) to other machines. For instance,such computing systems or devices may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment.Features of computing systems or devices may be embodied by a personalcomputer (PC), a tablet PC, a Personal Digital Assistant (PDA), acellular telephone, a web appliance, or any machine capable of executinginstructions (sequential or otherwise) that specify actions to be takenby that machine.

As also indicated above, the functionality discussed above may beimplemented by instructions, logic, or other information storage on amachine-readable medium. While the machine-readable medium may have beendescribed in various examples with reference to be a single medium, theterm “machine-readable medium” may include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more transitory ornon-transitory instructions or data structures. The term“machine-readable medium” shall also be taken to include any tangiblemedium that is capable of storing, encoding or carrying transitory ornon-transitory instructions for execution by the machine and that causethe machine to perform any one or more of the methodologies of thepresent disclosure, or that is capable of storing, encoding or carryingdata structures utilized by or associated with such instructions.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration but not by way of limitation, specificembodiments in which the disclosure can be practiced. These embodimentsare also referred to herein as “examples.” Such examples can includeelements in addition to those shown or described. However, thisdisclosure also contemplates examples in which only those elements shownor described are provided. Moreover, the disclosure also contemplatesexamples using any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a,” “an,” “the,” and “said” are used whenintroducing elements of aspects of the disclosure or in the embodimentsthereof, as is common in patent documents, to include one or more thanone or more of the elements, independent of any other instances orusages of “at least one” or “one or more.” In this document, the term“or” is used to refer to a nonexclusive or, such that “A or B” includes“A but not B,” “B but not A,” and “A and B,” unless otherwise indicated.

In the appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein.” Also, in the following claims, the terms “comprising,”“including,” and “having” are intended to be open-ended to mean thatthere may be additional elements other than the listed elements, suchthat after such a term (e.g., comprising, including, having) in a claimare still deemed to fall within the scope of that claim. Moreover, inthe following claims, the terms “first,” “second,” and “third,” etc.,are used merely as labels, and are not intended to impose numericalrequirements on their objects.

The present disclosure also relates to a computing system adapted,configured, or operated for performing the operations herein. Thissystem may be specially constructed for the required purposes, or it maycomprise a general purpose computer selectively activated orreconfigured by a computer program (e.g., instructions, code, etc.)stored in the computer. The order of execution or performance of theoperations in embodiments of the disclosure illustrated and describedherein is not essential, unless otherwise specified. That is, theoperations may be performed in any order, unless otherwise specified,and embodiments of the disclosure may include additional or feweroperations than those disclosed herein. For example, it is contemplatedthat executing or performing a particular operation before,contemporaneously with, or after another operation is within the scopeof aspects of the disclosure.

In view of the above, it will be seen that the several objects of thedisclosure are achieved and other beneficial results attained. Havingdescribed aspects of the disclosure in detail, it will be apparent thatmodifications and variations are possible without departing from thescope of aspects of the disclosure as defined in the appended claims. Asvarious changes could be made in the above constructions, products, andmethods without departing from the scope of aspects of the disclosure,it is intended that all matters contained in the above description andshown in the accompanying drawings shall be interpreted as illustrativeand not in a limiting sense.

The examples described herein may be implemented in a variety ofembodiments. For example, one embodiment includes a computing deviceincluding processing hardware (e.g., a processor or other processingcircuitry) and memory hardware (e.g., a storage device or volatilememory) including instructions embodied thereon, such that theinstructions, which when executed by the processing hardware, cause thecomputing device to implement, perform, or coordinate the electronicoperations for these techniques and system configurations. Anotherembodiment discussed herein includes a computer program product, such asmay be embodied by a machine-readable medium or other storage device,which provides the transitory or non-transitory instructions toimplement, perform, or coordinate the electronic operations for thesetechniques and system configurations. Another embodiment discussedherein includes a method operable on processing hardware of thecomputing device, to implement, perform, or coordinate the electronicoperations for these techniques and system configurations.

In further embodiments, the logic, commands, or transitory ornon-transitory instructions that implement aspects of the electronicoperations described above, may be provided in a distributed orcentralized computing system, including any number of form factors forthe computing system such as desktop or notebook personal computers,mobile devices such as tablets, netbooks, and smartphones, clientterminals and server-hosted machine instances, and the like. Anotherembodiment discussed herein includes the incorporation of the techniquesdiscussed herein into other forms, including into other forms ofprogrammed logic, hardware configurations, or specialized components ormodules, including an apparatus with respective means to perform thefunctions of such techniques. The respective algorithms used toimplement the functions of such techniques may include a sequence ofsome or all of the electronic operations described above, or otheraspects depicted in the accompanying drawings and detailed descriptionbelow.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the disclosure without departing fromits scope. While the dimensions, types of materials, and exampleparameters, functions, and implementations described herein are intendedto define the parameters of the disclosure, they are by no meanslimiting and are exemplary embodiments. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

Also, in the above Detailed Description, various features may be groupedtogether to streamline the disclosure. This should not be interpreted asintending that an unclaimed disclosed feature is essential to any claim.Rather, inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment. The scope of the disclosure should bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method comprising: accessing a database oftraining data comprising prior radiotherapy treatment plans; defining aparametrized function for generating a synthetic computerized tomography(sCT) image from a given medical image; defining a loss function that isbased on a derivative of a dose; training the parametrized functionbased on the loss function by operating the parametrized function onbatches of the training data; and providing the trained parametrizedfunction when a stopping criteria is met, the stopping criteria beingbased on a computed gradient of the loss function with respect toparameters of the parametrized function.
 2. The method of claim 1,further comprising applying the trained parametrized function on a newmedical image to generate a new sCT image corresponding to the newmedical image.
 3. The method of claim 1, wherein the parametrizedfunction comprises a machine learning model.
 4. The method of claim 1,wherein training the parametrized function comprises: obtaining a firstbatch of data comprising training medical images and training sCT imagescorresponding to the training medical images; and applying theparametrized function to the first batch of data to estimate anintermediate sCT image.
 5. The method of claim 4, further comprising:computing a derivative or gradient of a first dose based on theintermediate sCT image; computing a derivative or gradient of a seconddose based on a given one of the training sCT images; and computing theloss function based on the derivative or gradients of the first andsecond doses.
 6. The method of claim 5, further comprising updating theparameters of the parametrized function based on the loss function. 7.The method of claim 1, further comprising training another parameterizedfunction for generating a radiotherapy treatment plan from a medicalimage and a segmentation map or distance map.
 8. The method of claim 1,further comprising: processing radiotherapy treatment plan informationto estimate one or more radiotherapy treatment plan parameters based ona process that depends on an output of a subprocess that estimates aderivative of a dose calculation, wherein the derivative of the dosecalculation is used in the parametrized function, wherein the derivativeof the dose calculation is computed with respect to at least one of oneor more radiation parameters or one or more geometry parameters of aradiotherapy treatment device.
 9. The method of claim 8, wherein theparametrized function is trained, based on a plurality of training dosecalculations, to establish a relationship between a plurality oftraining radiotherapy treatment plan information and a plurality oftraining radiotherapy treatment plan parameters.
 10. The method of claim1, wherein the parametrized function includes a deep neural network,wherein the given medical image comprises at least one of a trainingmagnetic resonance (MR) image, a training cone-beam computed tomography(CBCT) image, a training computed tomography (CT) image.
 11. Anon-transitory computer-readable medium comprising non-transitorycomputer-readable instructions, the computer-readable instructionscomprising instructions for performing operations comprising: accessinga database of training data comprising prior radiotherapy treatmentplans; defining a parametrized function for generating a syntheticcomputerized tomography (sCT) image from a given medical image; defininga loss function that is based on a derivative of a dose; training theparametrized function based on the loss function by operating theparametrized function on batches of the training data; and providing thetrained parametrized function when a stopping criteria is met, thestopping criteria being based on a computed gradient of the lossfunction with respect to parameters of the parametrized function. 12.The non-transitory computer-readable medium of claim 11, wherein theoperations further comprise applying the trained parametrized functionon a new medical image to generate a new sCT image corresponding to thenew medical image.
 13. The non-transitory computer-readable medium ofclaim 11, wherein the parametrized function comprises a machine learningmodel.
 14. The non-transitory computer-readable medium of claim 11,wherein training the parametrized function comprises: obtaining a firstbatch of data comprising training medical images and training sCT imagescorresponding to the training medical images; and applying theparametrized function to the first batch of data to estimate anintermediate sCT image.
 15. The non-transitory computer-readable mediumof claim 14, wherein the operations further comprise: computing aderivative or gradient of a first dose based on the intermediate sCTimage; computing a derivative or gradient of a second dose based on agiven one of the training sCT images; and computing the loss functionbased on the derivative or gradients of the first and second doses. 16.A system comprising: a memory for storing instructions; and one or moreprocessors for executing the instructions stored in the memory forperforming operations comprising: accessing a database of training datacomprising prior radiotherapy treatment plans; defining a parametrizedfunction for generating a synthetic computerized tomography (sCT) imagefrom a given medical image; defining a loss function that is based on aderivative of a dose; training the parametrized function based on theloss function by operating the parametrized function on batches of thetraining data; and providing the trained parametrized function when astopping criteria is met, the stopping criteria being based on acomputed gradient of the loss function with respect to parameters of theparametrized function.
 17. The system of claim 16, wherein theoperations further comprise applying the trained parametrized functionon a new medical image to generate a new sCT image corresponding to thenew medical image.
 18. The system of claim 16, wherein the parametrizedfunction comprises a machine learning model.
 19. The system of claim 16,wherein training the parametrized function comprises: obtaining a firstbatch of data comprising training medical images and training sCT imagescorresponding to the training medical images; and applying theparametrized function to the first batch of data to estimate anintermediate sCT image.
 20. The system of claim 19, wherein theoperations further comprise: computing a derivative or gradient of afirst dose based on the intermediate sCT image; computing a derivativeor gradient of a second dose based on a given one of the training sCTimages; and computing the loss function based on the derivative orgradients of the first and second doses.